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Underwater Target bearing only tracking Based on First Order Divided Difference Filter 25th

Underwater Target Bearing Only Tracking Based on First Order...

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第25届中国控制与决策会议

作者： Xu Jinlong Wang Hongjian Zhang Aihua College of Automation Harbin Engineering University

ISBN: (纸本)9781467355339

The passive sonar based target tracking is of great importance in practical applications. In underwater bearings only target tracking research, the traditional extended kalman filtering method uses Tarlor expansion to achieve the system nonlinear terms linearization, and it leads to a complicated computing time and singularity problems. This paper presents a method based on the First Order Divided Differential Filter （DDF1） algorithm for solving the problem. The DDF1 is derived based on polynomial approximations obtained with multidimensional Stirling interpolation formula, the implementation is significantly simpler because of no derivatives is required. The results of simulation and consistency test showed that the proposed algorithm has high tracking precision and tracking performance compared with the traditional EKF method under the same simulation conditions.

关键词： Underwater target bearing only tracking First order divided difference filter (DDF1) Extended kalman filter (EKF) and Consistency testing

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A Kalman Filter with Progressive Measurement Update

A Kalman Filter with Progressive Measurement Update

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第37届中国控制会议

作者： Zhonghong Wu Zhangsong Shi Li Pan Weapon Eng. College Naval Uni. of Engineering Electronics Eng. College Naval Uni. of Engineering

Under the condition of large initial error and/or accurate measurement, due to the decrease of the accuracy of the joint Gauss distribution hypothesis, the moment approximation filtering methods based on Gauss hypothesis have the problem of posteriori error matrix overestimated, which means the error of the filter is much lower than the actual error, resulting in filtering divergence. In view of this problem, a Kalman filter with progressive measurement update is proposed. Firstly, based on Gauss hypothesis, under the framework of Daum-Huang filter, the progressive Bayesian formal description of the state evolution equation is given, the linear Gauss solution of the state evolution equation is derived. Secondly, by means of the first order Taylor expansion, the approximate solution of the nonlinear condition is obtained, and at last a Kalman filter with progressive measurement update and its nonlinear extend form are proposed. Simulation results show that in the large initial error and/or accurate measurement conditions, proposed method improves position estimation accuracy by 90% than UKF（Unscented Kalman Filter） and CKF（Cubature Kalman Filter）, by 17% than EKF（Extended Kalman Filter）, by 30% than similar method when progressive step is 0.1 and by 17% when progressive step is 0.01;proposed method improves velocity estimation accuracy by 66% than UKF and by 73% than CKF, by 53% than EKF, by 23% than similar method when progressive step is 0.1 and equivalent accuracy when progressive step is 0.01.

关键词： nonlinear filtering progressive Bayesian progressive measurement update extended Kalman filter bearing only tracking

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Application of an Improved Particle Filter for State Estimation

Application of an Improved Particle Filter for State Estimat...

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第二十七届中国控制会议

作者： Xiang Li,Liu Yu,Su Baoku Space Control & Inertial Technology Research Center,Harbin Institute of Technology,Harbin 150001,P.R.china

A novel Gaussian mixture Sigma-Point Particle Filter algorithm is proposed to mitigate the sample depletion *** posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted Expectation-Maximization *** simulation results demonstrate the validity of the proposed algorithm.

关键词： Monte carlo simulation Particle filter Estimation algorithm bearing only tracking

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